Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 2018

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Medical image registration in image guided surgery: Issues, challenges and research opportunities

Alam F., Rahman S. U., Ullah S., Gulati K.

Biocybernetics and Biomedical Engineering

2018

Vol. 38, no. 1

71--89

Multimodal images of a patient obtained at different time, pre-surgical planning, intra-procedural guidance and visualization, and post-procedural assessment are the core components of image-guided surgery (IGS). In IGS, the goal of registration is to integrate corresponding information in different images of the same organ into a common coordinate system. Registration is a fundamental task in IGS and its main purpose is to provide better visualization and navigation to the surgeons. In this paper, we describe the most popular types of medical image registration and evaluate their prominent state-of-the art issues and challenges in image-guided surgery. We have also presented the factors which affect the accuracy, reliability and efficiency of medical image registration methods. It is not possible to achieve highly successful IGS until all the issues and challenges in registration process are identified and subsequently solved.

Classification of rocks radionuclide data using machine learning techniques

Khan A. R., Mir A. A., Saeed S., Rafique M., Asim K. M., Iqbal T., Jabbar A., Rahman S. U.

Acta Geophysica

2018

Vol. 66, no. 5

1073--1079

The aim of this study is to assess the performance of linear discriminate analysis, support vector machines (SVMs) with linear and radial basis, classification and regression trees and random forest (RF) in the classification of radionuclide data obtained from three different types of rocks. Radionuclide data were obtained for metamorphic, sedimentary and igneous rocks using gamma spectroscopic method. A P-type high-purity germanium detector was used for the radiometric study. For analysis purpose, we have determined activity concentrations of 232Th, 226Ra and 40K radionuclides, published elsewhere (Rafique et al. in Russ Geol Geophys 55:1073–1082, 2014), in different rock samples and built the classification model after pre-processing the data using three times tenfold cross-validation. Using this model, we have classified the new samples into known categories of sedimentary, igneous and metamorphic. The statistics depicts that RF and SVM with radial kernel outperform as compared to other classification methods in terms of error rate, area under the curve and with respect to other performance measures.